The application of KAZE features to the classification echocardiogram videos

In the computer vision field, both approaches of SIFT and SURF are prevalent in the extraction of scale-invariant points and have demonstrated a number of advantages. However, when they are applied to medical images with relevant low contrast between target structures and surrounding regions, these approaches lack the ability to distinguish salient features. Therefore, this research proposes a different approach by extracting feature points using the emerging method of KAZE. As such, to categorise a collection of video images of echocardiograms, KAZE feature points, coupled with three popular representation methods, are addressed in this paper, which includes the bag of words (BOW), sparse coding, and Fisher vector (FV). In comparison with the SIFT features represented using Sparse coding approach that gives 72% overall performance on the classification of eight viewpoints, KAZE feature integrated with either BOW, sparse coding or FV improves the performance significantly with the accuracy being 81.09%, 78.85% and 80.8% respectively. When it comes to distinguish only three primary view locations, 97.44% accuracy can be achieved when employing the approach of KAZE whereas 90% accuracy is realised while applying SIFT features

The application of KAZE features to the classification echocardiogram videos

In the computer vision field, both approaches of SIFT and SURF are prevalent in the extraction of scale-invariant points and have demonstrated a number of advantages. However, when they are applied to medical images with relevant low contrast between target structures and surrounding regions, these approaches lack the ability to distinguish salient features. Therefore, this research proposes a different approach by extracting feature points using the emerging method of KAZE. As such, to categorise a collection of video images of echocardiograms, KAZE feature points, coupled with three popular representation methods, are addressed in this paper, which includes the bag of words (BOW), sparse coding, and Fisher vector (FV). In comparison with the SIFT features represented using Sparse coding approach that gives 72% overall performance on the classification of eight viewpoints, KAZE feature integrated with either BOW, sparse coding or FV improves the performance significantly with the accuracy being 81.09%, 78.85% and 80.8% respectively. When it comes to distinguish only three primary view locations, 97.44% accuracy can be achieved when employing the approach of KAZE whereas 90% accuracy is realised while applying SIFT features

High-speed multi-dimensional relative navigation for uncooperative space objects

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation

Alignment of Hyperspectral Images Using KAZE Features

Image registration is a common operation in any type of image processing, specially in remote sensing images. Since the publication of the scale–invariant feature transform (SIFT) method, several algorithms based on feature detection have been proposed. In particular, KAZE builds the scale space using a nonlinear diffusion filter instead of Gaussian filters. Nonlinear diffusion filtering allows applying a controlled blur while the important structures of the image are preserved. Hyperspectral images contain a large amount of spatial and spectral information that can be used to perform a more accurate registration. This article presents HSI–KAZE, a method to register hyperspectral remote sensing images based on KAZE but considering the spectral information. The proposed method combines the information of a set of preselected bands, and it adapts the keypoint descriptor and the matching stage to take into account the spectral information. The method is adequate to register images in extreme situations in which the scale between them is very different. The effectiveness of the proposed algorithm has been tested on real images taken on different dates, and presenting different types of changes. The experimental results show that the method is robust achieving image registrations with scales of up to 24.0×This research was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia [grant numbers GRC2014/008 and ED431G/08] and Ministerio de Educación, Cultura y Deporte [grant number TIN2016-76373-P] both are co–funded by the European Regional Development Fund. The work of Álvaro Ordóñez was supported by the Ministerio de Educación, Cultura y Deporte under an FPU Grant [grant number FPU16/03537]. This work was also partially supported by Consejería de Educación, Junta de Castilla y León (PROPHET Project) [grant number VA082P17]S

Adding Cues to Binary Feature Descriptors for Visual Place Recognition

In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present two concrete applications of our methodology, demonstrating the two considered types of cues. In addition to that, we conducted on these applications a broad quantitative and comparative evaluation covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.Comment: 8 pages, 8 figures, source: www.gitlab.com/srrg-software/srrg_bench, submitted to ICRA 201